RF high-power amplifiers, which are used in mobile radio base stations, for example, have a characteristic which is curved and thus highly non-linear in the region of high output power levels close to the 1 dB compression point. As a result, signals with large amplitudes are distorted and/or chopped (AM/AM conversion). Furthermore, the phase of the emitted signal is also shifted (AM/PM conversion). In order to avoid drastic broadening of the transmission spectrum, and hence adjacent channel interference, as well as a deterioration in the modulation accuracy, and the considerable increase in the bit error rate associated therewith, only the linear part of the amplifier characteristic is typically used. However, this is worthwhile only for low power levels. In the case of RF amplifiers for base stations for second and third generation mobile radio, if there is a restriction to the linear part of the amplifier characteristic, it would be necessary to use amplifiers with two to ten times the power, as a result of which the production costs for the equipment would be increased enormously. Hence, the efficiency levels of the amplifiers would be considerably reduced. Furthermore—depending on the semiconductors used—the inter-modulation characteristics would become worse.
In order to avoid this, the nonlinear characteristic can be compensated for by suitable distortion of the input signal. This is commonly referred to as pre-distortion and, until now, has been used primarily at the analog level and in the small signal area, although it has also been used in digital baseband. This is generally done rigidly, for example by using diode characteristics.
A neural network which is in the form of a “perceptron” and which is connected upstream of a power amplifier in the signal path is known from “A Neural Network Approach To Data Predistortion With Memory In Digital Radio Systems”, by Benvenuto et al., Proceedings Of The International Conference On Communications (ICC.), Geneva, May 23-26, 1993 New York, IEEE, US. Coefficients for an FIR filter for pre-distortion are determined by means of the perceptron.
Furthermore, adaptive pre-distortion methods are also known, for example from the article “Adaptive Digital Pre-distortion Linearization” in “Microwaves & RF” 1996 pages 270 to 275, in which the ACTUAL transmission signal at the amplifier output is measured in order to compensate for the actual non-linearity of the amplifier characteristic. This is compared with the NOMINAL transmission signal at the amplifier input. Using known mathematical methods (for example by means of regression, error polynomial, etc.), the required pre-distortion of the input signal can then be determined from the difference.
These known methods, and the apparatuses used to carry them out, have the disadvantage that the required function, which is aimed at the linearization of the amplifier characteristic, cannot be defined flexibly, but only as an error polynomial that is to be minimized.
Furthermore, it is impossible to include not only the optimum approximation of the amplifier characteristic but also the transmission spectrum in the required function.
Finally, it is impossible to approximate the nonlinear characteristic of the amplifier, and/or its inverse characteristic, optimally, when the measurement signal to be evaluated is subject to severe noise.